WO2019003576A1

WO2019003576A1 - Road surface state estimating method and road surface state estimating device

Info

Publication number: WO2019003576A1
Application number: PCT/JP2018/015451
Authority: WO
Inventors: 剛真砂
Original assignee: 株式会社ブリヂストン
Priority date: 2017-06-28
Filing date: 2018-04-12
Publication date: 2019-01-03
Also published as: JP2019006351A; US11187645B2; EP3647144A1; CN110869261A; EP3647144A4; US20200173908A1

Abstract

This road surface state estimating device is provided with: an acceleration sensor 11 installed in a tire; acceleration information acquiring means 12, 13, 14 for acquiring acceleration information which is inputted from an output of the acceleration sensor 11 to the tire; a storage means 15 for storing acceleration information 15M for each preset road surface roughness; and a road surface state estimating means 16 for estimating a road surface state by comparing the acquired acceleration information with the acceleration information stored for each road surface roughness in the storage means 15.

Description

Road surface state estimation method and road surface state estimation device

The present invention relates to a method and apparatus for estimating the uneven state of a road surface.

When the road surface on which the vehicle is traveling is a so-called bad road with large unevenness, the torque applied to the drive system including the transmission fluctuates significantly. Therefore, if the unevenness of the road surface on which the vehicle is traveling can be estimated, and the driving force can be controlled in accordance with the estimated unevenness of the road surface, the vehicle can be traveled stably.
Conventionally, as a method of estimating the uneven state of the road surface, the wheel speed which changes according to the unevenness of the road surface is detected, and when the fluctuation exceeds a predetermined threshold, the road surface on which the vehicle is traveling is a bad road There is a method of determining that there is a certain condition or a method of determining the unevenness of the road surface based on a filtered value obtained by filtering the rotational acceleration of the rotating member which rotates with a fixed relationship with the wheel or the wheel. It is proposed (for example, refer patent document 1).

JP 2004-138549 A

However, in the method described in Patent Document 1 above, since the unevenness state of the road surface is estimated from the vibration transmitted to the rotating member through the tire, not only the large unevenness can be detected, but also the degree of the road unevenness is accurate. There is a problem that it is difficult to estimate well.

The present invention has been made in view of the conventional problems, and it is an object of the present invention to provide a method and an apparatus capable of accurately estimating the degree of unevenness of a road surface.

As a result of intensive investigations, the inventor of the present invention has largely changed the output waveform of the acceleration sensor installed in the tire depending on the degree of unevenness of the road surface on which the tire is traveling. It has been found that the degree of unevenness can be accurately estimated, and the present invention has been achieved.
That is, the present invention is a method of estimating the state of a road surface on which a tire is traveling, which is a first step of acquiring acceleration information to be input to the tire by an acceleration sensor installed in the tire; A second step of comparing acceleration information with acceleration information for each road surface roughness set in advance, and a third step of estimating the state of the road surface from the comparison result Do.

Further, the present invention is an apparatus for estimating the state of a road surface on which a tire is traveling, and includes an acceleration sensor installed in the tire and acceleration information for acquiring acceleration information to be input to the tire from the output of the acceleration sensor. Obtaining means, storage means for storing acceleration information for each road surface roughness set in advance, and comparing the acquired acceleration information with acceleration information for each road surface roughness stored in the storage means, And E. road surface condition estimating means for estimating the condition of the road surface.

The summary of the invention does not enumerate all necessary features of the present invention, and a subcombination of these feature groups can also be an invention.

It is a figure showing the composition of the road surface state estimating device concerning this embodiment. It is a figure which shows the example of attachment of an acceleration sensor. It is a figure which shows an example of the extraction method of the acceleration waveform of the area | region before depression. It is a figure which shows the calculation method of 10 point average roughness Rz. It is a figure which shows the relationship between road surface roughness and an acceleration waveform. It is a figure which shows the relationship between road surface roughness and R.M.S value of the acceleration in the area | region before depression. It is a flowchart which shows the road surface state estimation method by this invention. It is a figure which shows the calculation method of another parameter | index of road surface roughness.

Embodiment FIG. 1 is a diagram showing a configuration of a road surface state estimation device 10 according to the present embodiment.
The road surface condition estimation device 10 includes an acceleration sensor 11, an acceleration waveform extraction unit 12, a pre-depression waveform extraction unit 13, an acceleration information calculation unit 14, a storage unit 15, and a road surface condition estimation unit 16. Estimate the road surface roughness of the road surface on which you are driving.
The acceleration waveform extraction means 12, the pre-depression waveform extraction means 13, and the acceleration information calculation means 14 constitute an acceleration information acquisition means.
Each means from the acceleration waveform extraction means 12 to the road surface state estimation means 16 is constituted by, for example, computer software and a storage device such as a RAM.
As shown in FIG. 2, the acceleration sensor 11 is disposed at the center of the inner liner portion 21 of the tire 20 in the tire width direction so that the detection direction is the tire circumferential direction, and the tire circumference is input from the road surface to the tire tread 22. Detect directional acceleration.
The acceleration waveform extraction means 12 extracts a tire radial acceleration waveform (hereinafter referred to as an acceleration waveform) which is a time-series waveform of tire circumferential acceleration output from the acceleration sensor 11 as shown in FIG. 3. The length of the acceleration waveform to be extracted is a length including at least two peaks appearing at the depression point P _f or a length including at least two peaks appearing at the kicking point P _k . Thereby, the rotation time T which is the time required for the said tire 20 to make 1 rotation can be calculated | required from an acceleration waveform.
As shown in FIG. 5, when the degree of unevenness of the road surface is large, it is difficult to identify the peak position with the waveform as it is, so for example, an acceleration waveform passing through a low pass filter with a cutoff frequency of several hundred kHz or less The positions of the stepping point P _f and the kicking point P _k may be specified using this.
The pre-depression waveform extraction means 13 extracts an acceleration waveform in the pre-depression area from the acceleration waveform extracted by the acceleration waveform extraction means 12. Here, the pre-depression area refers to an area before the depression point P _{f and in} which the area width T _f is at most 0.45 × T. Incidentally, in this example, T _f = 0.3 × T, but in order to obtain a necessary amount of information, it is preferable to set the region width T _f to T _f 0.030.03 × T.

The acceleration information calculation means 14 calculates acceleration information from the acceleration waveform of the pre-depression area extracted by the pre-depression waveform extraction means 13.
In this example, the RMS value S of acceleration is used as the acceleration information.
S = (a ₁ ² + a ₂ ² + a ₃ ² +... + A _N ² ) ^1/2 × (1 / N)
Here, a _k is an acceleration at t = t _k , and N is a sampling number.
The storage unit 15 stores an Rz-S map 15M which is acceleration information for each road surface roughness, which is obtained in advance.
Here, Rz is a 10-point average surface roughness that is an index of the road surface roughness of the road surface on which the tire travels, and S is the RMS value of the acceleration at that time.
As shown in FIG. 4, the 10-point average roughness Rz [mm] is extracted from the roughness curve indicating the state of the unevenness of the road surface by the reference length L (here 40 mm) in the direction of the average line. The absolute value of the mean value of the mean (X) of the peak heights from the highest peak to the fifth peak (X) and the mean value of the mean value of the heights of the lowest valley bottoms to the fifth bottom (Y) from the mean line of the extracted part It can be expressed in sum.
Rz [mm] = | X ₁ + X ₂ + X ₃ + X ₄ + X ₅ | / 5 + | Y ₁ + Y ₂ + Y ₃ + Y ₄ + Y ₅ |
On the other hand, S _R is a vehicle equipped with acceleration sensors, in the case that was run on a road surface of different 10-point average roughness Rz, the acceleration information calculated from the acceleration waveform of depression front region (RMS value of acceleration) . In this example, a vehicle equipped with a tire having a tire size of 195 / 65R15 is made to travel at a speed of 50 km / h, and the RMS value S of acceleration for each road surface roughness is obtained.
The differences in acceleration waveform due to Rz are shown in FIGS. 5 (a) to 5 (d).
As shown in the figure, it can be seen that the amplitude of acceleration increases as Rz increases. This is because the rougher the road surface roughness, the larger the input from the road surface to the tire.
Figure 6 is a diagram showing the Rz-S _R map 15M, as is apparent from the figure, exhibits a high correlation (R ² = 0.9774) and the ten-point average roughness Rz and the RMS value of the acceleration, the By using the Rz-S map, the road surface condition can be accurately estimated.
The road surface condition estimating means 16 obtains the 10-point average roughness Rz from the RMS value S of the acceleration calculated by the acceleration information calculating means 14 and the Rz-S map stored in the storage means 15 to obtain the tire Estimate the road surface roughness of the road surface on which you are driving. For example, if S = 8.2 [G], it can be estimated that the 10-point average roughness Rz of the road surface is 4.

Next, the road surface condition estimation method according to the present invention will be described with reference to the flowchart of FIG.
First, after the tire circumferential acceleration signal input to the tire 20 is detected by the acceleration sensor 11 (step S10), an acceleration waveform is extracted from the tire circumferential acceleration signal (step S11).
Then, from the extracted acceleration waveform, the time interval between the peaks appearing at two kicking points P _k adjacent in time is determined, and it is the time required for the tire 20 to make one rotation. The rotation time T is set (step S12).
Next, an acceleration waveform in the pre-depression region is extracted from the acceleration waveform (step S13), and an RMS value S of acceleration which is acceleration information is calculated from the extracted acceleration waveform in the pre-depression region (step S14).
Next, the Rz-S map 15M, which is acceleration information for each road surface roughness stored in advance in the storage unit 15, is compared with the RMS value S of the calculated acceleration to obtain an index of the road surface roughness. By obtaining a certain 10-point average roughness Rz, the state of the road surface on which the tire is traveling (road surface roughness) is estimated (step S15).

As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It is obvious to those skilled in the art that various changes or modifications can be added to the above embodiment. It is also apparent from the scope of the claims that the embodiments added with such alterations or improvements can be included in the technical scope of the present invention.

For example, in the above embodiment, the degree of unevenness of the road surface is estimated from the tire circumferential acceleration, but tire radial acceleration may be used. That is, since the input from the road surface can be separated into the input in the tire circumferential direction and the input in the tire radial direction, the degree of unevenness of the road surface can be accurately estimated even using the tire radial acceleration.
In the above embodiment, the road surface roughness is estimated using the RMS value S of the acceleration in the pre-depression area extracted from the acceleration waveform in the pre-depression area, but the acceleration waveform from before the pre-depression to after the kick-out is used It is also good. However, since the acceleration level after kicking out from the contact surface is easily influenced by the structure of the tire and the tread pattern, it is preferable to use the acceleration waveform in the region before stepping in, as in this example.
Further, in the above embodiment, although the acceleration information is set as the RMS value S of the acceleration in the pre-depression region, it is corrected by the tire rotational speed (or vehicle speed) instead of the RMS value S of the acceleration as shown below. By using the RMS value S ', the degree of unevenness of the road surface can be estimated more accurately.
S '= S × T ² ,
T: Rotation time of tire In the above embodiment, the index of road surface roughness is a 10-point average roughness Rz, but an index of road surface roughness other than Rz such as average roughness Rα may be used. The average roughness Rα is the distance between the peak value Z _k (k = 1 to N; N is the number of irregularities in the reference length L) of the irregularities in the reference length L shown in FIG. _{If k} |, then Rα [mm] = (| Z ₁ | + | Z ₂ | +... + | Z _N |) / N.
As shown in FIG. 8, as the peak value Z _k , only the unevenness having a peak value Z _k whose distance to the average line Z _m is larger than _a predetermined threshold value Za (> 0) is selected, and the average line is selected. The average value of the distance to Z _m may be used as an index of road surface roughness.
Alternatively, | Z _max |, which is the maximum value of | Z _k |, may be used as an index of road surface roughness.

In summary, it can be described as follows. That is, the present invention is a method of estimating the state of a road surface on which a tire is traveling, which is a first step of acquiring acceleration information to be input to the tire by an acceleration sensor installed in the tire; A second step of comparing acceleration information with acceleration information for each road surface roughness set in advance, and a third step of estimating the state of the road surface from the comparison result Do.
As described above, since the unevenness state of the road surface is estimated from the vibration information (acceleration information) acting on the tire that is in direct contact with the road surface, the degree of the unevenness of the road surface can be accurately estimated.
Further, the acceleration information acquired in the first step or the acceleration information to be compared in the second step is information of an acceleration waveform before stepping in the acceleration waveform detected by the acceleration sensor. .
Thus, the degree of unevenness of the road surface can be estimated without being affected by the structure of the tire or the tread pattern, so that the estimation accuracy of the degree of unevenness of the road surface can be further improved.
Further, the present invention is an apparatus for estimating the state of a road surface on which a tire is traveling, and acquiring acceleration information to be input to the tire from an acceleration sensor installed in the tire and an output of the acceleration sensor. Means, storage means for storing acceleration information for each road surface roughness set in advance, and the acquired acceleration information and acceleration information for each road surface roughness stored in the storage means are compared, And road surface condition estimating means for estimating the condition of the road surface.
By adopting such a configuration, it is possible to obtain a road surface state estimation device capable of accurately estimating the degree of unevenness of the road surface.

10 road surface condition estimation device, 11 acceleration sensors,
12 acceleration waveform extraction means, 13 pre-depression waveform extraction means,
14 acceleration information calculation means, 15 storage means,
15M Rz-S map, 16 road surface condition estimation means,
20 tires, 21 inner liner, 22 tire treads.

Claims

A method of estimating the condition of a road surface on which a tire is traveling,
A first step of acquiring acceleration information input to the tire by an acceleration sensor installed in the tire;
A second step of comparing the acquired acceleration information with acceleration information for each road surface roughness set in advance;
A third step of estimating the state of the road surface from the comparison result;
A road surface state estimation method comprising:
The acceleration information acquired in the first step or the acceleration information to be compared in the second step is information of an acceleration waveform before stepping in the acceleration waveform detected by the acceleration sensor. The road surface state estimation method according to claim 1.
An apparatus for estimating the condition of a road surface on which a tire is traveling,
An acceleration sensor installed in the tire,
Acceleration information acquisition means for acquiring acceleration information to be input to a tire from the output of the acceleration sensor;
Storage means for storing acceleration information for each road surface roughness set in advance;
Road surface condition estimation means for estimating the state of the road surface by comparing the acquired acceleration information with acceleration information for each road surface roughness stored in the storage means;
A road surface state estimation device comprising: